Molecular Docking is used to positioning the computer-generated 3D structure of small
ligands into a receptor structure in a variety of orientations, conformations and positions. This
method is useful in drug discovery and medicinal chemistry providing insights into molecular
recognition. Docking has become an integral part of Computer-Aided Drug Design and Discovery
(CADDD). Traditional docking methods suffer from limitations of semi-flexible or static treatment
of targets and ligand. Over the last decade, advances in the field of computational, proteomics and
genomics have also led to the development of different docking methods which incorporate
protein-ligand flexibility and their different binding conformations. Receptor flexibility accounts
for more accurate binding pose predictions and a more rational depiction of protein binding
interactions with the ligand. Protein flexibility has been included by generating protein ensembles
or by dynamic docking methods. Dynamic docking considers solvation, entropic effects and also
fully explores the drug-receptor binding and recognition from both energetic and mechanistic point
of view. Though in the fast-paced drug discovery program, dynamic docking is computationally
expensive but is being progressively used for screening of large compound libraries to identify the
potential drugs. In this review, a quick introduction is presented to the available docking methods
and their application and limitations in drug discovery.
Type II topoisomerases
like DNA gyrase initiate ATP-dependent
negative
supercoils in bacterial DNA. It is critical in all of the bacteria
but is missing from eukaryotes, making it a striking target for antibacterials.
Ciprofloxacin is a clinically approved drug, but its clinical effectiveness
is affected by the emergence of resistance in both Gram-positive and
Gram-negative bacteria. Thus, it is vital to identify novel compounds
that can efficiently inhibit DNA gyrase, and quantitative structure–activity
relationship (QSAR) modeling is a quick and economical means to do
so. A QSAR-based virtual screening approach was applied to identify
new gyrase inhibitors using an
in-house
-generated
combinatorial library of 29828 compounds from seven ciprofloxacin
scaffold structures. QSAR was built using a data set of 271 compounds,
which were identified as positive and negative inhibitors from existing
data reported in
in vitro
studies. The best QSAR
model was developed using the 5-fold cross-validation Neural Network
in Orange, and it was based on five PaDEL descriptors with an accuracy
and sensitivity of 83%. As a result of screening of an
in-house
-built combinatorial library with the best-developed QSAR model,
675 compounds were identified as potential inhibitors of DNA gyrase.
These inhibitors were further docked with DNA gyrase using AutoDock
to compare the binding mode and score of the selected/screened compounds,
and 615 compounds exhibited a docking score comparable to or lower
than that of ciprofloxacin. Out of these, the top five analogues 902b,
9699f, 4419f, 5538f, and 898b reported in our study have binding scores
of −13.81, −12.95, −12.52, −12.43, and
−12.41 kcal/mol, respectively. The MD simulations of these
five analogues for 100 ns supported the interaction stability of analogues
with
Escherichia coli
DNA gyrase. Ninety-one
per cent of the analogues screened by the QSAR model displayed better
binding energy than ciprofloxacin, demonstrating the efficacy of the
generated model. The NN-QSAR model proposed in this manuscript can
be downloaded from
.
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